Sentiment Analysis of Reviews from Google Play: Azur Lane, Genshin Impact, Arknights - Siregar, Kardandi Alfarizi; Cahyadi, Bhagaskara; Samosir, Legiman; Azhard, Alfani; Supiyandi (Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies, 2025)
Sentiment analysis of user reviews for mobile games is essential for understanding player perceptions of a game. This study focuses on sentiment analysis of user reviews for three popular mobile games: Azur Lane, Genshin Impact, and Arknights, using the Support Vector Machine (SVM) algorithm. The objective of this research is to classify reviews into three sentiment categories: Positive, Negative, and Neutral. Data was collected through web scraping from the Google Play Store, with a total of 6,000 reviews analyzed. The data preprocessing steps included cleaning, tokenization, stopword removal, and stemming, followed by TF-IDF feature extraction. The results show that the SVM model achieved an accuracy of 79.50%, with the best performance for Positive and Negative sentiments, but struggled with Neutral sentiment classification. The sentiment distribution revealed that Azur Lane had a higher proportion of Negative reviews compared to Genshin Impact and Arknights, which received predominantly Positive feedback. This study provides insights into the potential of using SVM for sentiment analysis in mobile games, and highlights areas for improvement, such as better handling of Neutral sentiment through more advanced models or balanced datasets.